This data-set contains 100 english documents. The document set is created for generating summary.

Series of Incident data and summary statistics reports produced which provide statistical information on incidents by type, year, geographical location, and others. The data provided is that from the Hazardous Materials Incident Report Form 5800.1

The authors used the TL;DR dataset, which consists of reddit posts with summaries.

Series of Incident data and summary statistics reports produced which provide statistical information on incidents by type, year, geographical location, and others. The data provided is that from the Hazardous Materials Incident Report Form 5800.1

This dataset was used in the paper 'Template-based Abstractive Microblog Opinion Summarisation' (to be published at TACL, 2022). The data is structured as follows: each file represents a cluster of tweets which contains the tweet IDs and a summary of the tweets written by journalists. The gold standard summary follows a template structure and depending on its opinion content, it contains a main story, majority opinion (if any) and/or minority opinions (if any). Additionally, we will include the abstractive model baselines we have used in the paper.For ease of use, we distinguish between opinionated/non-opinionated and training/testing/agreement sets.Due to the recent changes in the availability of the Twitter / X academic API, please reach out to iman.bilal@warwick.ac.uk if you consider using the dataset.License: The annotations are provided under a CC-BY license, while Twitter retains the ownership and rights of the content of the tweets.

Summary of all information relevant to 2,938 transcripts. This file contains the following information: gene and transcript IDs, k-means and super-cluster assignments, various transcript features related to sequence length, conserved domains, CPC and CodeWise results related to coding potential, and Mercator-based annotation. Most information in other supplemental Tables is derived from this file to facilitate further data mining. (XLSX 1589 kb)

Mining: Summary Series: General Summary: Inventories with LIFO Valuation by Subsector and Industries: 2012.

The dataset is intended for summarizing texts that can be used in extractive or abstractive approaches. This data is from year 2018-2022 and has three categories attraction, hotel, and restaurant. Each category consists of 100 different objects, resulting in a total of 300 objects across all categories. Each object has 5 reviews and 1 ground truth. The ground truth is a summary reference created by 3 experts, with 2 individuals holding bachelor's degrees in Indonesian Language and Literature Education and having worked as teachers for more than 2 years. The remaining person holds a bachelor's degree in Indonesian Literature and has 2 years of experience as an NLP annotator. Each category folder, such as the 'attraction' folder, contains 4 subfolders, each of which holds 25 objects.

These spreadsheets include a Techno-Economic Analysis (TEA) summary and descriptions and links to mining data analyzed as part of this study. The TEA summary includes the results from several mining data-informed geothermal development models analyzed using the DOE's Geothermal Electricity Technology Evaluation Model (GETEM).

Summary of ANOVA results among the time points within the clusters.

Mining and Quarrying Volume: Natural and Associated Gas (NA) data was reported at 691.574 Cub m bn in 2017. This records an increase from the previous number of 640.000 Cub m bn for 2016. Mining and Quarrying Volume: Natural and Associated Gas (NA) data is updated yearly, averaging 637.000 Cub m bn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 691.574 Cub m bn in 2017 and a record low of 571.100 Cub m bn in 1997. Mining and Quarrying Volume: Natural and Associated Gas (NA) data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Mining and Quarrying Sector – Table RU.BAD002: Mining and Quarrying: Natural Gas: Summary.

Russia Mining and Quarrying: OG: Crude Oil data was reported at 44,232.500 Ton th in Dec 2016. This records an increase from the previous number of 42,786.600 Ton th for Nov 2016. Russia Mining and Quarrying: OG: Crude Oil data is updated monthly, averaging 40,590.600 Ton th from Jan 2005 (Median) to Dec 2016, with 144 observations. The data reached an all-time high of 44,665.900 Ton th in Mar 2016 and a record low of 34,335.500 Ton th in Feb 2005. Russia Mining and Quarrying: OG: Crude Oil data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Mining and Quarrying Sector – Table RU.BAB001: Mining and Quarrying: Crude Oil: Summary.

Summary for length of stay of the hospitalized patients.

Mining: Subject Series: Materials Summary: Selected Supplies, Minerals Received for Preparation, Purchased Machinery, and Fuels Consumed by Type of Industry: 2012.

Russia Mining and Quarrying: Crude Oil incl Gas Condensate: Crude Oil data was reported at 514.364 Ton mn in 2017. This records an increase from the previous number of 512.994 Ton mn for 2016. Russia Mining and Quarrying: Crude Oil incl Gas Condensate: Crude Oil data is updated yearly, averaging 452.500 Ton mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 514.364 Ton mn in 2017 and a record low of 293.000 Ton mn in 1996. Russia Mining and Quarrying: Crude Oil incl Gas Condensate: Crude Oil data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Mining and Quarrying Sector – Table RU.BAB001: Mining and Quarrying: Crude Oil: Summary.

This report summarizes the ServCat pilot project and offers recommendations for the full-scale implementation of the database. During the pilot project a total of 2,473 documents from 10 different refuges were entered into the ServCat database. A document can take anywhere from 5 to 60 minutes to scan. Overall, a general approximation is that it takes 30 minutes to scan a document and enter it as a record in ServCat. Several lessons were learned throughout the course of the pilot project that will help guide the implementation of the data mining effort at the full-scale. Full-scale implementation of the ServCat database will require collaboration between refuges, regions, and the Natural Resource Program Center. Standardizing the metadata entered in ServCat will make it easier to find a document in the database and faster to create a record. Templates, the master keyword list, and the ServCat Guidance should be used whenever possible to expedite data entry.

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Text Analytics Market Size 2024-2028

The text analytics market size is forecast to increase by USD 18.08 billion, at a CAGR of 22.58% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of Service-Oriented Architecture (SOA) among end-users. SOA's flexibility and scalability make it an ideal choice for text analytics applications, enabling organizations to process vast amounts of unstructured data and gain valuable insights. Additionally, the ability to analyze large volumes of unstructured data provides valuable insights through data analytics, enabling informed decision-making and competitive advantage. Furthermore, the emergence of advanced text analytical tools is expanding the market's potential by offering enhanced capabilities, such as sentiment analysis, entity extraction, and topic modeling. However, the market faces challenges that require careful consideration. System integration and interoperability issues persist, as text analytics solutions must seamlessly integrate with existing IT infrastructure and data sources.
Ensuring compatibility and data exchange between various systems can be a complex and time-consuming process. Addressing these challenges through strategic partnerships, standardization efforts, and open APIs will be essential for market participants to capitalize on the opportunities presented by the market's growth.

What will be the Size of the Text Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by advancements in technology and the increasing demand for insightful data interpretation across various sectors. Text preprocessing techniques, such as stop word removal and lexical analysis, form the foundation of text analytics, enabling the extraction of meaningful insights from unstructured data. Topic modeling and transformer networks are current trends, offering improved accuracy and efficiency in identifying patterns and relationships within large volumes of text data. Applications of text analytics extend to fake news detection, risk management, and brand monitoring, among others. Data mining, customer feedback analysis, and data governance are essential components of text analytics, ensuring data security and maintaining data quality.

Text summarization, named entity recognition, deep learning, and predictive modeling are advanced techniques that enhance the capabilities of text analytics, providing actionable insights through data interpretation and data visualization. Machine learning algorithms, including machine learning and deep learning, play a crucial role in text analytics, with applications in spam detection, sentiment analysis, and predictive modeling. Syntactic analysis and semantic analysis offer deeper understanding of text data, while algorithm efficiency and performance optimization ensure the scalability of text analytics solutions. Text analytics continues to unfold, with ongoing research and development in areas such as prescriptive modeling, API integration, and data cleaning, further expanding its applications and capabilities.

The future of text analytics lies in its ability to provide valuable insights from unstructured data, driving informed decision-making and business growth.

How is this Text Analytics Industry segmented?

The text analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premises


Component

  Software
  Services


Geography

  North America

    US


  Europe

    France
    Germany


  APAC

    China
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Text analytics is a dynamic and evolving market, driven by the increasing importance of data-driven insights for businesses. Cloud computing plays a significant role in its growth, as companies such as Microsoft, SAP SE, SAS Institute, IBM, Lexalytics, and Open Text offer text analytics software and services via the Software-as-a-Service (SaaS) model. This approach reduces upfront costs for end-users, as they do not need to install hardware and software on their premises. Instead, these solutions are maintained at the company's data center, allowing end-users to access them on a subscription basis. Text preprocessing, topic modeling, transformer networks, and other advanced techniques are integral to text analytics.

Fake news detection, spam filtering, sentiment analysis, and social media monitoring are essential applications. Deep learning, machine l

Summary of data sources for cropping events.

Blockchain data query: Stacks miners summary